System And Method Of Providing Mask Defect Printability Analysis

ABSTRACT

A simulated wafer image of a physical mask and a defect-free reference image are used to generate a severity score for each defect, thereby giving a customer meaningful information to accurately assess the consequences of using a mask or repairing that mask. The defect severity score is calculated based on a number of factors relating to the changes in critical dimensions of the neighbor features to the defect. A common process window can also be used to provide objective information regarding defect printability. Certain other aspects of the mask relating to mask quality, such as line edge roughness and contact corner rounding, can also be quantified by using the simulated wafer image of the physical mask.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.11/770,682 entitled “System And Method Of Providing Mask DefectPrintability Analysis” filed Jun. 28, 2007 which is a continuation ofU.S. patent application Ser. No. 11/043,398 entitled “System And MethodOf Providing Mask Defect Printability Analysis” filed Jan. 25, 2005 andissued as U.S. Pat. No. 7,254,251 which is a continuation of U.S. patentapplication Ser. No. 09/814,023 entitled “System And Method Of ProvidingMask Defect Printability Analysis” filed Mar. 20, 2001 and issued asU.S. Pat. No. 6,873,720.

FIELD OF THE INVENTION

An inspection providing defect printability analysis for an integratedcircuit mask is described.

BACKGROUND OF THE INVENTION Mask/Reticle Defects

To fabricate an integrated circuit (IC) in a semiconductor substrate, aphysical representation of the IC is transferred onto a pattern tool.Then, the pattern tool is exposed to transfer this pattern onto thesemiconductor substrate. A mask is a standard pattern tool used in ICprocessing. Typically, a mask includes patterns that can be transferredto the entire semiconductor substrate (for example, a wafer) in a singleexposure. A reticle, another standard pattern tool, must be stepped andrepeated to expose the entire substrate surface. For ease of referenceherein, the term “mask” refers to either a reticle or a mask.

A typical mask is formed from a quartz plate having a chrome coating.Generally, a mask is created for each layer of the IC design.Specifically, a portion of the IC layout data file representing aphysical layer (such as a polysilicon layer or a metal layer) is etchedinto the chrome layer. Thus, each mask includes the pattern thatrepresents the desired circuit layout for its corresponding layer. Inhigh density ICs, a mask can also include optical proximity correction(OPC) features, such as serifs, hammerheads, bias, and assist bars.These OPC features are sub-resolution features used to compensate forprocess artifacts and/or proximity effects.

In high-density IC designs, those skilled in the art of IC fabricationhave recognized the importance of using masks that provide accuraterepresentations of the original design layout. Unfortunately, a“perfect” mask is not commercially viable. In fact, even under optimalmanufacturing conditions, some mask defects can occur outside thecontrolled process.

A defect on a mask is any deviation from the design database (i.e. anirregularity) that is deemed unacceptable by an inspection tool or aninspection engineer. FIG. 1 illustrates a flowchart 100 of a prior artmethod of inspecting an integrated circuit. In step 110, an IC isdesigned. In step 112, a data file of mask design data, e.g. a layout ofthe IC, is created. This data is used to manufacture the mask in step114. At this point, the mask is inspected in step 116 by scanning thesurface of the mask with a high-resolution microscope and capturingimages of the mask. Irregularities in the mask are identified in a listby their location. In one embodiment, the mask has an associated gridpattern and the list designates the squares in the grid pattern in whichthe irregularities are located. This inspection and irregularityidentification can be performed by specialized equipment/softwareprovided by companies such as KLA-Tencor or Applied Materials.

To determine whether the mask passes inspection (step 118), a skilledinspection engineer or a semi-automated inspection device reviews theirregularities identified in step 116. Note that only irregularitiesdeemed to be outside the tolerances set by the manufacturer or user arecharacterized as defects. If irregularities are discovered and areoutside tolerances, then a determination is made in step 128 if the maskcan be repaired. If the mask can be repaired, then the mask is cleanedand/or repaired in step 130 and the process returns to step 116 ofinspecting the mask. If the mask cannot be repaired, then a new maskmust be manufactured and the inspection process returns to step 114. Ifthe mask passes inspection, as determined in step 118, then an actualwafer is exposed using the mask in step 120.

To ensure that the mask has produced the desired image on the wafer, thewafer itself is typically inspected in step 122. If irregularities arediscovered and are outside tolerances as determined in inspection step124, then a determination is made in step 128 if the mask can berepaired. If the mask can be repaired, then the mask is cleaned and/orrepaired in step 130 and the process returns to step 116 of inspectingthe mask. If the mask cannot be repaired, then a new mask must bemanufactured and the inspection process returns to step 114. Ifirregularities on the wafer are discovered, but are determined to bewithin tolerances, then the mask passes inspection in step 124 and theinspection process ends in step 126.

Unfortunately, the above-described process has a number of significantdisadvantages. For example, an automated inspection device measurestolerance principally by size. Thus, if a pinhole on the mask has apredetermined size, then the automated inspection device would probablydesignate the pinhole as a defect regardless of its location on themask. In contrast, a skilled inspection engineer can use additional,more subjective methods based on his/her level of experience.Specifically, an experienced engineer might be able to determine whethera pinhole of even less than the predetermined size but in a criticalarea would have an adverse effect on functionality or performance andtherefore should be characterized a defect, or whether a pinhole greaterthan the predetermined size but not in a critical area would not affectfunctionality or performance. However, this skill set must be developedover time at considerable expense. Moreover, like all human activity,even after developing this skill set, the quality of review inevitablyvaries. Thus, the step of characterizing the irregularity is prone toerror.

Another disadvantage of the above-described process is the triggering offalse defect detections. For example, an automated inspection device canfalsely report an OPC or an imperfect OPC feature as a defect. As notedpreviously, an OPC feature is a sub-resolution feature used tocompensate for proximity effects. Therefore, the OPC feature wouldtypically not constitute nor contribute to a defect.

Mask Inspection System

To address these disadvantages, a mask inspection system designed byNumerical Technologies, Inc. provides mask quality assessment withoutresorting to an actual exposure of a wafer. This mask inspection systemis described in U.S. patent application Ser. No. 09/130,996 (hereinreferenced as the NTI system), entitled, “Visual Inspection andVerification System”, which was filed on Aug. 7, 1998 and isincorporated by reference herein.

FIG. 2 illustrates a process 200 of inspecting a mask for defects inaccordance with the NTI system. Process 200 utilizes an inspection tool202 and a wafer image generator 209. In one embodiment, inspection tool202 includes an image acquirer 203, typically a high resolution imagingdevice, to scan all or a portion of a physical mask 201. A defectdetection processor 204 compares the mask images provided by imageacquirer 203 to a set of potential defect criteria and determines whatareas of the mask contain potential defects. If a potential defect isidentified, defect detection processor 204 signals a defect area imagegenerator 205 to provide a defect area image of the area including andsurrounding the potential defect.

In one embodiment, inspection tool 202 then provides defect area imagedata 206 to wafer image generator 209. In another embodiment, this datais digitized by digitizing device 207, stored in storage device 208, andthen provided to wafer image generator 209 at a later point in time. Inyet another embodiment that analyzes both areas identified as potentialdefects and areas not identified as potential defects, the scanned imageprovided by image acquirer 203 can be provided directly to wafer imagegenerator 209 or indirectly through digitizing device 207 and storagedevice 208.

Wafer image generator 209 includes an input device 210 that receivesdata directly from inspection tool 202 in a real time feed or off-linedata from storage device 208. An image simulator 211 receives theinformation from input device 210 as well as other input data, such aslithography conditions 212. Lithography conditions 212 can include, butare not limited to, the wavelength of illumination, the numericalaperture, the coherence value, the defocus (wherein the term defocus asused herein refers to focal plane positioning), the exposure level, lensaberrations, substrate conditions, and the required critical dimension.Using these inputs, image simulator 211 can generate a wafer image 213that simulates physical mask 201 being exposed on a wafer. Imagesimulator 211 can also generate a simulated process window 214, andperformance output 215. In one embodiment, image simulator 211 alsotakes into account the photoresist and/or the etching process asindicated by block 216.

Although process 200 provides valuable information to the customer viathe simulated wafer image 213, for example, the customer must stillreview that information to make a determination regarding theappropriate action to take (e.g. repair the mask or fabricate a newmask). Thus, process 200 can be subject to human error. Therefore, aneed arises for a mask inspection system and process that provides anobjective, accurate measure of mask defect printability and maskquality.

SUMMARY OF THE INVENTION

A system and method for analyzing defect printability is provided. Inthis analysis, a physical mask and a corresponding, defect-freereference image are inspected. In one embodiment, the defect-freereference image can be one of the following: a simulated image of thelayout of the physical mask, a defect-free area of the physical maskhaving the same pattern, or a simulated image of the physical mask as itis processed in manufacturing.

This inspection identifies any defects, i.e. irregularities, of thephysical mask compared to the reference image. If a defect isidentified, a defect area image of the defect and the area surroundingthe defect from the physical mask as well as the corresponding areaimage from the reference image are provided to a wafer image generator.The wafer image generator generates simulations of the image data, i.e.for the physical mask and reference image.

In one embodiment, the wafer image generator can receive a plurality oflithography conditions. These conditions include data that is specificto the lithography conditions and system parameters under which thephysical mask will be exposed by the customer. Such data could include,for example, the wavelength of the illumination being used in the system(λ), the numerical aperture of the system (NA), the coherency value ofthe system (σ), the illumination type (e.g. off-axis or annular), thedefocus, the exposure level, the lens aberrations, the substrateconditions, and the critical dimension (CD) of the design. In oneembodiment, each parameter could include a range of values, therebyallowing the wafer image generator to generate a plurality ofsimulations based on a range of possible lithography conditions indifferent combinations.

Sub-wavelength production flow, which is highly non-linear, can becompensated for in the following manner. Specifically, to enhance theaccuracy of the wafer images in sub-wavelength technology, the waferimage generator can also receive one or more conversion factors. Theconversion factor can vary based on features on the mask, such asisolated lines, densely packed lines, and contacts. The conversionfactor can also vary based on certain aspects of the fabricationprocess, including the stepper parameters and the photoresist.

In one embodiment, a test pattern provided on a test mask is simulatedusing the wafer image generator. The test pattern could include isolatedlines of varying widths, densely pack lines of varying widths, andcontacts of various sizes. Defect analysis, including any changes incritical dimension (CD), can be noted for each test pattern on thesimulated wafer image. Note that as used herein, the CD is a measurementor calculated size of a specified location, which can be one-dimensionalor two-dimensional. From this information, the conversion factor foreach feature can be accurately calculated. Moreover, any number ofsimulations can be provided using various processes (e.g. lithographyconditions) to obtain the conversion factors for these fabricationprocesses. A mask shop specific bias can also be included in thelithography conditions, thereby further enhancing the accuracy of theconversion factors generated by this embodiment.

This method of providing conversion factors is extremely cost-effectivebecause it eliminates the cost associated with the printed wafer as wellas the time for fabrication of that wafer. Moreover, because of thesimulation environment, this method provides significant flexibility inoptimizing system parameters before actual fabrication.

In accordance with one embodiment, a defect printability analysisgenerator receives the simulated wafer images of the physical mask andthe reference image from the wafer image generator. In one embodiment,the two simulated wafer images are aligned in a pre-processingoperation. Alignment can be done using defect free patterns in the maskor using coordinates from the masks. When these patterns or coordinatesare aligned, the features provided on those masks (as well as on waferimages of those masks) are also aligned.

After alignment, two-dimensional analysis can proceed. Intwo-dimensional analysis, a defect on the simulated wafer image of thephysical mask and the corresponding area on the simulated wafer image ofthe reference mask are identified. Then, any feature proximate to thedefect (a neighbor feature) on the simulated wafer image of the physicalmask is identified. In one simple implementation, any feature within apredetermined distance of the defect can be identified as a neighborfeature. In another embodiment, both the size of the defect and thedistance of the defect from the neighbor feature are compared tomeasurements in a design rule table. The design rule table can identify,for each defect size (or range of sizes), a maximum distance from thedefect, wherein if the feature is located less than the maximum distancefrom the defect, then the feature is a neighbor feature. Finally, anyidentified neighbor features are located on the simulated wafer image ofthe reference mask.

At this point, defect analysis on the simulated wafer images can bedone. Defect analysis includes determining an average CD deviation(ACD), a relative CD deviation (RCD), and a maximum CD deviation (MCD).To calculate the ACD, a CD of a defect-free feature on the simulatedwafer image of the physical mask is subtracted from the CD of thecorresponding feature on the simulated wafer image of the referencemask. This difference is then divided by the CD of the correspondingfeature on the simulated wafer image of the reference mask. Thiscalculation generates a CD deviation of the simulated wafer physicalimage from the simulated wafer reference image (i.e. a calibrationfactor used for later calculations). For greater accuracy, more than onedefect-free area can be analyzed to provide the ACD for defect-freeareas. In one embodiment, ACDs are calculated for each exposure.

To calculate the relative CD deviation (RCD), a CD of an identifiedneighbor feature on the simulated wafer image of the reference mask issubtracted from the CD of the corresponding feature on the simulatedwafer image of the physical mask. Note that a feature can beone-dimensional, such as a line or space, or two-dimensional, such as acontact hole, pile, post, serif, or some other area-based structure.This difference is then divided by the CD of the corresponding featureon the simulated wafer image of the reference mask. In one embodiment,the RCD can be calculated for each neighbor feature and for eachexposure. The maximum CD deviation (MCD) among the RCDs can then bedetermined for each exposure level.

In accordance with one embodiment, the defect printability analysisgenerator can also receive information from a critical regionidentification generator. The critical region identification generatorprovides the defect printability analysis generator with informationidentifying areas of each mask that are designated critical regions,such as gates, that require a high degree of precision to ensure properperformance in the final IC device. This information is referred to asthe tolerance for CD deviations (TCD). A defect in a critical regiontypically has a lower TCD than a defect in a non-critical region.

In accordance with one feature, a defect severity score (DSS) can becalculated using the average CD deviations (ACD), the maximum CDdeviations (MCDs), the tolerance for CD deviations (TCD), and a variableN indicating the total number of exposures used. One exemplary equationfor calculating this defect severity score is:

${D\; S\; S} = {( {3/N} ) \times {\overset{N}{\sum\limits_{1}}{{( {{M\; C\; D_{i}} - ( {A\; C\; {D_{i}/3}} )} )/T}\; C\; D}}}$

In one embodiment, the defect printability analysis generator outputs aDSS having a scale from 1 to 10 in an impact report. This impact reportcan be used to reduce human error in defect printability analysis. Forexample, perhaps a predetermined DSS score could indicate that theprinted features (as simulated by the inspection system) would havesignificant performance issues, but that repair of the physical mask ispossible. On the other hand, perhaps a higher DSS score than above couldindicate not only performance issues, but that re-fabrication of thephysical mask is recommended. Thus, by providing a numerical resulthaving an associated meaning for each number, a technician can proceedefficiently and without error to the next action, e.g. repair of thephysical mask or re-fabrication of the physical mask.

In another feature, defect printability can also be objectively assessedusing various process windows. Illustrative process windows are thoseprovided by plots of defocus versus exposure deviation or depth of focusversus exposure latitude. Curves on these plots represent areasincluding a defect as well as defect-free areas. The largest rectanglefitting within these curves is termed the exposure defocus window,wherein a common process window is the intersection of multiple exposuredefocus windows. Focus and exposure values that fall within the commonprocess window produce resist features, e.g. CDs, within tolerance,whereas focus and exposure values that fall outside the process windowproduce resist features outside tolerance. Thus, analyzing the processwindows associated with a feature can provide an objective means ofdetermining the printability of that feature based on the proximity of adefect. In one embodiment, the defect printability analysis generatorcould determine a common process window for the features provided inphysical and reference masks and provide this information in the impactreport.

The impact report can be advantageously used to analyze repairs thatcould be done on a physical mask. Specifically, using the impact report(or portions therein), a bitmap editor can indicate possible correctionsto be made to the physical mask to eliminate or significantly minimizethe effects of one or more defects. The bitmap editor can then output asimulated mask including these corrections (the repaired mask).

Then, the repaired mask can be inspected by the inspection tool and usedby the wafer image generator to generate a new, simulated wafer imageand a new impact report that indicates the success of the possiblecorrections provided in the repaired mask. If the corrections areacceptable, then the bitmap editor can provide the correctioninformation directly to the mask repair tools for repair of the physicalmask. If the customer desires additional optimization or analysis ofdifferent parameters, then the above-described processes can be repeateduntil either the corrections are deemed to be within an acceptable rangeor the bitmap editor indicates that the desired result cannot beattained by repairing the physical mask.

In one embodiment, the bitmap editor can also indicate the optimizedmask writing strategy, e.g. identify certain tools to be used forcertain defects. Additionally, bitmap editor can receive inputs thatindicate customer time or cost limitations, thereby allowing the bitmapeditor to optimize the repair process based on these customerparameters. In yet another embodiment of the invention, the bitmapeditor can be used to provide information to wafer repair tools.Specifically, the bitmap editor can include a program that compares theefficacy of repairing a mask versus repairing a wafer.

The defect printability analysis can be done on individual defects or ona plurality of defects. In one embodiment, the inspection tool and thewafer image generator can automatically provide outputs regarding alldefects found on the physical mask. Thus, the resulting impact reportcould include defect severity scores on all defects.

Alternatively, if desired, the impact report could include only defectseverity scores above a certain value. This tailored impact reportcould, in turn, be provided to the bitmap editor and subsequently to themask repair tools. Therefore, the inspection system could encompass acomplete, automated defect detection and correction process, therebysignificantly reducing the time for a mask to be analyzed and repaired(if appropriate).

The defect printability analysis also eliminates the necessity ofevaluating OPC features separately from other features. If an OPCfeature prints (as determined by simulated wafer image) due to a defect,then the defect defect analysis can indicate this error as CD changesare determined. Thus, by eliminating any complicated design rulesregarding OPC features, the inspection system ensures a quick, reliable,and accurate method to identify defects adversely affecting OPCfeatures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a prior art mask inspection process.

FIG. 2 illustrates a known mask inspection process and system developedby Numerical Technologies, Inc.

FIG. 3 illustrates a method of analyzing defects by using multiplemasks.

FIGS. 4A and 4B illustrate analyzing defects based on their location inrelation to various features on the mask.

FIG. 5 illustrates a mask inspection process and system.

FIG. 6 illustrates one method to generate accurate conversion factors.

FIG. 7 illustrates another method to generate accurate conversionfactors.

FIGS. 8A-8C illustrate various features of a computer-implementedprogram associated with the defect printability analysis generator.

FIGS. 9A and 9B illustrate portions of a physical mask and a referencemask, respectively.

FIGS. 10A(1-3) illustrate simulated wafer images of a defect-free areaof the physical mask in FIG. 9A for three exposures.

FIGS. 10B(1-3) illustrate simulated wafer images of a defect-free areaof the reference mask in FIG. 9B for three exposures.

FIGS. 11A(1-3) illustrate simulated wafer images of a defect area of thephysical mask in FIG. 9A for three exposures.

FIGS. 11B(1-3) illustrate simulated wafer images of a defect area of thereference mask in FIG. 9B for three exposures.

FIG. 12A illustrates a mask including a feature and a defect proximateto the feature.

FIG. 12B illustrates a plot of feature size versus defocus for thefeature of FIG. 12A.

FIG. 12C illustrates a plot of exposure deviation versus defocus and acommon process window plot for the feature of FIG. 12A.

FIG. 12D illustrates a plot of exposure latitude versus depth of focusfor the feature of FIG. 12A.

FIG. 13A illustrates a mask including a feature and a defect proximateto the feature, wherein this defect is larger than the defect of FIG.12A.

FIG. 13B illustrates a plot of feature size versus defocus for thefeature of FIG. 13A.

FIG. 13C illustrates a plot of exposure deviation versus defocus for thefeature of FIG. 13A.

FIG. 13D illustrates a plot of exposure latitude versus depth of focusfor the feature of FIG. 13A.

FIG. 14A illustrates a mask including a feature having a defectintegrally formed therein.

FIG. 14B illustrates a plot of feature size versus defocus for thefeature of FIG. 14A.

FIG. 14C illustrates a plot of exposure deviation versus defocus for thefeature of FIG. 14A.

FIG. 14D illustrates a plot of exposure latitude versus depth of focusfor the feature of FIG. 14A.

FIG. 15A illustrates a mask including a feature having a defectintegrally formed therein, wherein this defect is larger than the defectof FIG. 14A.

FIG. 15B illustrates a plot of feature size versus defocus for thefeature of FIG. 15A.

FIG. 15C illustrates a plot of exposure deviation versus defocus for thefeature of FIG. 15A.

FIG. 15D illustrates a plot of exposure latitude versus depth of focusfor the feature of FIG. 15A.

FIG. 16A illustrates a mask including a contact (or via or post).

FIG. 16B illustrates a plot of feature size versus defocus for thecontact of FIG. 16A.

FIG. 16C illustrates a plot of exposure deviation versus defocus for thecontact of FIG. 16A.

FIG. 16D illustrates a plot of exposure latitude versus depth of focusfor the contact of FIG. 16A.

FIG. 17A illustrates a mask including a contact (or via or post) havingsignificant critical dimension (CD) variation.

FIG. 17B illustrates a plot of feature size versus defocus for thecontact of FIG. 17A.

FIG. 17C illustrates a plot of exposure deviation versus defocus for thecontact of FIG. 17A.

FIG. 17D illustrates a plot of exposure latitude versus depth of focusfor the contact of FIG. 17A.

FIG. 18 illustrates a mask repair process and system.

FIG. 19A illustrates a simplified layout showing a line with line edgeroughness that might not exhibit critical dimension variations.

FIG. 19B illustrates a simplified layout in which line edge roughness isdetermined.

FIGS. 20A and 20B illustrate a simplified layout in which cornerrounding and/or symmetry is determined.

DETAILED DESCRIPTION OF THE DRAWINGS Introduction

In accordance with an inspection system/process, all irregularities,i.e. potential defects, are characterized as actual defects. In oneembodiment, a severity score is provided for each defect, thereby givinga customer meaningful information to accurately assess the consequencesof using a mask or repairing that mask. The defect severity score iscalculated based on a number of factors relating to the changes in thecritical dimensions of features proximate to the defect. In anotherembodiment, process windows can be used to provide objective informationregarding mask defect printability. Certain other aspects of the maskrelating to mask quality, such as line edge roughness and contact cornerrounding, can also be quantified.

Layout of the IC: Identify Critical Regions

FIG. 3 illustrates a feature that facilitates identifying criticalregions of an IC. Specifically, a simplified process 300 includesidentifying a defect in a mask and using at least one other mask todetermine whether the defect is located in a critical region. Forexample, mask 301 represents a polysilicon region 310 of one layer in anIC. Two defects 304 and 305 are identified on polysilicon region 310.Note that both defects are identical in size. Mask 302 represents adiffusion region 311 of another layer in the IC.

Process 300 includes determining the size and location of the defects inrelation to features in various masks, such as masks 301 and 302. Forexample, defects 304 and 305, when viewed solely with respect topolysilicon region 310 on mask 301, could be deemed insubstantial by aprior art inspection device, which typically determines defects by size.In contrast, process 300, in addition to size, considers the location ofdefects 304 and 305 in relation to diffusion region 311 provided on mask302. Specifically, process 300 uses information from various masks toidentify critical regions of the IC. A composite IC layout 303identifies the overlap of polysilicon region 310 and diffusion region311 as a critical region 306. As a key feature of the final IC, criticalregion 306, i.e. a gate, requires a high degree of precision to ensureproper performance of the transistor in the final IC device. Thus, byanalyzing multiple masks and the features therein, defect 305 could becharacterized as insubstantial because it is small and in a non-criticalregion (e.g. the interconnect), whereas defect 305 could becharacterized as substantial even though it is small because it is in acritical region of the IC (e.g. the gate).

As described below in detail, a defect in a critical region willtypically have a higher defect severity score than a defect in anon-critical region.

CD Variations: Identify Defect and Neighboring Features

FIG. 4A illustrates a simplified mask 400 representing variouspolysilicon features of one layer in an IC. Mask 400 includes threedefects 401, 402, and 403 that might affect neighboring polysiliconfeatures 404 and 405. In this example, assume that defects 401, 402, and403 are identical in size.

In general, a defect typically has more impact in a crowded region thanin a less-crowded region. Thus, a defect located in an area defined byfeatures a distance X apart can have more printability impact than adefect located in an area defined by features distance Y apart, assumingthat distance X is less than distance Y. However, this general rule hassignificant limitations.

Referring to FIG. 4B, each defect can be analyzed according to itslocation relative to neighboring features. For example, assume thatdefect 401 is located a distance d1(A) from feature 405 and a distanced1(B) from feature 404, wherein distance d1(A) is substantially equal todistance d1(B). Assume further that defect 403 is located a distanced3(A) from feature 405 and a distance d3(B) from feature 404, whereindistance d3(A) is substantially equal to distance d3(B). In thisexample, defect 401 would have a greater printability impact on mask 400than defect 403. Therefore, the general rule applies to defects 401 and403.

However, mask 400 also includes a defect 402 located a distance d2(A)(i.e. zero) from feature 405 and a distance d2(B) from feature 404. Inthis case, defect 402 could have more printability impact on feature 405than defect 401. Moreover, defect 402 probably has less printabilityimpact on feature 404 than defect 403. Thus, a general rule limited tothe spacing of features does not provide an accurate indication ofprintability impact.

One possible solution to this problem is to measure the distances toneighboring features (such as d1, d2, and d3) from each defect (such asdefects 401, 402, and 403, respectively). These distances in combinationwith a measurement of the size of the defect could be factored into aplurality of design rules to provide the printability impact. However,this analysis is computationally intensive, thereby increasing the timerequired to provide meaningful information to the customer. Moreover,even if the size of the defect and the distance of the defect from theneighboring feature are known, the actual impact of the defect on theneighboring feature cannot fully be predicted by mere inspection of themask.

Defect Printability Analysis

Therefore, in accordance with one embodiment, a limited number ofvariables are analyzed. In one embodiment, this limited number ofvariables includes the critical dimension (CD) of the mask.Specifically, any CD changes in features that occur because of theproximity of a defect can be determined. To analyze these CD changes,the mask image can be simulated, as described in reference to FIG. 5.

FIG. 5 illustrates a process 500 for analyzing defect printability. Inprocess 500, a physical mask 501A and a reference mask 501B are analyzedby an inspection tool 502. In one embodiment, reference mask 501B can bea physical mask having the same layout as physical mask 501A, but havingno defects. In another embodiment, reference mask 501B can be asimulated image from a layout of physical mask 501A.

In one embodiment, an inspection tool 502 includes an image acquirer 503to scan all or a portion of a physical mask 501A and correspondingportions of reference mask 501B. Image acquirer 503 may include ahigh-resolution imaging device such as a high-resolution opticalmicroscope, a scanning electron microscope (SEM), a focus ion beam, anatomic force microscope, or a near-field optical microscope. Imageacquirer 503 may also include an interface device for digitizing theimage information from the imaging device. In one embodiment, theinterface device includes a CCD camera that generates a gray scale bitimage that represents the image.

A defect detection processor 504 compares the images from physical mask501A and reference mask 501B provided by image acquirer 503 andidentifies any defects of physical mask 501A. In one embodiment, defectdetection processor 504 includes a computer running a program ofinstructions for scanning masks 501. If a defect is identified, defectdetection processor 504 signals an image generator 505 to provide animage of the defect and the area surrounding the defect from physicalmask 501A as well as the corresponding area from reference mask 501B.Image generator 505 also provides an image of a defect-free area fromboth masks 501. In one embodiment, image generator 505 can provide animage including both the defect area and the defect-free area. Tofacilitate the defect printability analysis described below in detail,the coordinates of these defect and defect-free areas can be transmittedwith the generated area image data. Note that if reference mask 501B isprovided as a simulated layout and if a complete image of physical mask501A is to be generated, then the simulated layout file of referencemask 501B can be provided directly to image generator 505 as indicatedby line 506B.

In one embodiment, inspection tool 502 then provides both area imagedata from physical mask 501A and reference mask 501B to a wafer imagegenerator 509 in a real-time data feed, as indicated by line 506D. Inanother embodiment, this data is digitized by digitizing device 507,stored in a storage device 508, and then provided to wafer imagegenerator 509 at a later point in time. Storage device 508 can storethis digitized information in a format such as Windows BMP on any typeof appropriate media including a computer hard disk drive, a CDROM, anda server. In yet another embodiment that analyzes physical mask 501A inits entirety, the scanned image(s) provided by image acquirer 503 can beprovided to image generator 505 as indicated by line 506A or todigitizing device 507 as indicated by line 506C.

Wafer image generator 509 includes an input device 510 and an imagesimulator 511. Input device 510 typically includes hardware for readingthe type of image data from inspection tool 502 and/or from storagedevice 508, such as any known, digitizing image grabber (for a real-timedata feed) provided by Matrox™, Meteor™, or Pulsar™. In one embodiment,image simulator 511 includes a computer-implemented program runningWindows/DOS at 200 MHz on an appropriate platform, such as a personalcomputer or a workstation, having at least 64 MB of memory. Imagesimulator 511 receives the image data from input device 510 andgenerates simulations of the image data, i.e. for physical mask 501A andreference mask 501B. These simulations are referenced herein as waferimage (Phy) (for physical mask) 517A and wafer image (Ref) (forreference mask) 517B.

In one embodiment, image simulator 511 further receives a plurality oflithography conditions 512. These conditions include data that isspecific to the lithography conditions and system parameters under whichphysical mask 501A will be exposed by the customer. Such data couldinclude, for example, the wavelength of the illumination being used inthe system (λ), the numerical aperture of the system (NA), the coherencyvalue of the system (σ), the type of illumination (e.g. off-axis orannular), the defocus, the exposure level, the lens aberrations, thesubstrate conditions, and the critical dimension (CD) of the design. Inone embodiment, each parameter could include a range of values, therebyallowing image simulator 511 to generate a plurality of simulationsbased on a range of possible lithography conditions in differentcombinations. For example, this could be done by Monte Carlo simulationwith different types of distribution, such as Gaussian distribution.Thus, wafer image (Phy) 517A and wafer image (Ref) 517B can representthe simulated images which physical mask 501A and reference mask 501B(or portions thereof) would generate if an optical lithography exposurehad been performed under the same conditions as lithography conditions512.

Conversion Factors

For above (or near) wavelength designs, design rules for features usedin the layout can typically scale simultaneously by the same factor. Inthe event that some rules do not scale as fast as the other rules, minormodifications, typically performed within a relatively short time, canbe made to the database. However, in contrast, manufacturing steps insub-wavelength production flow are highly non-linear. Specifically, anymask errors can be amplified in the printed pattern on the wafer, andconsequently can adversely affect final device performance.

Therefore, to enhance the accuracy of wafer images 517 in sub-wavelengthtechnology, image simulator 511 can also receive a conversion factor 513in accordance with one embodiment. In one instance, the conversionfactor is called a mask error enhancement factor (MEEF).

If the conversion factor is “known”, then a multiplication of the maskCDs by the conversion factor can be done. Currently, “known” conversionfactors are typically theoretical estimations. However, thesetheoretical estimations can be inaccurate for a number of reasons.First, as recognized by Applicants, the conversion factor can vary basedon features on the mask. For example, the conversion factor for isolatedlines can be different from the conversion factor of densely packedlines. Moreover, the conversion factor for a contact can be differentfrom the conversion factors of either isolated lines or densely packedlines. Second, in addition to design issues on the mask, all aspects ofthe fabrication process including the stepper and the photoresist, forexample, can affect the conversion factor for a particular feature onthe mask. Therefore, theoretical estimations, which fail to account fordesign issues and process parameters, are inherently inaccurate.

Alternatively, if theoretical estimations are not accurate, then actualwafers can be fabricated and the device CDs can be measured on the waferusing SEM to determine the conversion factor(s). However, this processtypically includes printing and measuring tens or even hundreds of maskfeatures to measure and calculate the conversion factor(s). Therefore,this process is extremely expensive and, thus, commercially impractical.

A number of cost-effective solutions to the above-described problems canbe provided. FIG. 6 illustrates one method 600 to generate accurateconversion factors. In method 600, a test pattern can be provided on atest mask in step 601. The test pattern could include isolated lines ofvarying widths, densely packed lines of varying widths, and contacts ofvarious sizes. At this point, a single wafer can be printed in step 602.Defect analysis, including any changes in CD, can be noted for each testpattern on the wafer in step 603. From this information, the conversionfactor for each feature can be accurately calculated in step 604. Alimited number of additional wafers can also be printed using variousprocesses to obtain the conversion factors for these fabricationprocesses.

Note that the wafer printed from the test mask also includesshop-specific information, which could affect the conversion factor.Specifically, process variations can, and typically do, vary from oneshop to another shop. This variation can result in some CD changes onthe wafer, which is generally termed “bias” in the industry. By printinga wafer or a limited number of wafers at the shop and using a test maskas described above, a customer can either verify the shop's publishedbias or independently determine that shop's bias.

The test mask having the above-described test pattern provides the userwith an accurate conversion factor as well as shop bias, therebyallowing the customer to potentially compensate for unacceptable CDchanges (typically either in the design process, in the mask correctionoperation (described in further detail below), or by choosing adifferent shop).

FIG. 7 illustrates another method 700 to generate accurate conversionfactors. In method 700, a test pattern can be provided on a test mask instep 701. Similarly to method 600, this test pattern could also includeisolated lines of varying widths, densely packed lines of varyingwidths, and contacts of various sizes. At this point, a wafer image fromthe test mask can be simulated in step 702 using image simulator 511(FIG. 5). Defect analysis, including any changes in CD (explained indetail below), can be noted for each test pattern on the simulated waferimage in step 703. From this information, the conversion factor for eachfeature can be accurately calculated in step 704. Note that any numberof additional masks can be simulated using various processes (e.g.lithography conditions 512) to obtain the conversion factors for thesefabrication processes. Further note that the shop bias, described inreference to FIG. 6, can also be included in lithography conditions 512,thereby further enhancing the accuracy of the conversion factorsgenerated by this embodiment.

Method 700 is extremely cost-effective because it eliminates the costassociated with the printed wafer as well as the time for fabrication ofthat wafer. Moreover, because of the simulation environment, method 700provides significant flexibility in optimizing system parameters beforeactual fabrication.

Image Simulation

Image simulator 511 approximates the process of optical lithography byusing a simplified version of the Hopkins model as applied to integratedcircuit patterns. In this simplified version, the Hopkins model isviewed as a plurality of low pass filters that are applied to the inputdata. The output images from those low pass filters are added togenerate the simulated images (i.e. simulated wafer (Phy) image 517A andsimulated wafer (Ref) image 517B). Additional information regarding theHopkins model as used by image simulator 511 is provided in U.S. Ser.No. 09/130,996, and therefore is not described in detail herein.

Defect Severity Score Calculation

A defect printability analysis generator 515 receives simulated waferimages 517 from image simulator 511. Generator 515 includes acomputer-implemented program running Windows/DOS at 200 MHz on anappropriate platform, such as a personal computer or a workstation,having at least 64 MB of memory. In one embodiment, image simulator 511and generator 515 run on the same platform.

FIGS. 8A-8C illustrate various features of the computer-implementedprogram associated with generator 515. FIG. 8A indicates that a method800 of generating a defect severity score includes a pre-processing step810, a two-dimensional analysis step 820, a defect analysis step 830,and a critical area designation step 840.

In pre-processing step 810, simulated wafer (Phy) image 517A andsimulated wafer (Ref) image 517B are aligned. Alignment can be doneusing defect-free patterns (assuming the simulated images 517 includeboth defect and defect-free areas) or the coordinates provided by imagegenerator 505 for the defect/defect-free areas (and subsequentlytransmitted to input device 510, image simulator 511, and finally defectprintability analysis 515). When these patterns/areas are aligned, thefeatures provided on simulated images 517 are also aligned.

After alignment, two-dimensional analysis step 820 can proceed.Specifically referring to FIG. 8B, which describes two-dimensionalanalysis step 820 in further detail, a defect on simulated wafer (Phy)image 517A is identified in step 821. Then, the corresponding area ofsimulated wafer (Ref) image 517B is identified in step 822. Note thatthe coordinates provided by image generator 505 can be used foridentification steps 821 and 822. In step 823, any feature proximate tothe defect (also referenced herein as a neighbor feature) on simulatedwafer (Phy) image 517A is identified. Finally, in step 824, thecorresponding feature(s) on simulated wafer (Ref) image 517B can beidentified.

Note that the term “proximate” can refer to any feature that changes inCD as a result of the proximity of the defect. However, in one simpleimplementation, any feature within a predetermined distance of thedefect can be identified as a neighbor feature. In another embodiment,both the size of the defect (which can be determined in step 821) andthe distance of the defect from the neighbor feature (which can bedetermined in step 823) are compared to measurements in a design ruletable. The design rule table can identify, for each defect size (orrange of sizes), a maximum distance from the defect, wherein if thefeature is located less than the maximum distance from the defect, thenthe feature is characterized as a neighbor feature.

After two-dimensional analysis, defect analysis step 830 can proceed. Indefect analysis, the defect-free areas are analyzed to calculate averageCD deviations (ACDs) (explained in detail below) and the defect areasare analyzed to calculate the relative CD deviations (RCDs) (alsoexplained in detail below). Note that calculating the ACDs and RCDs canbe done in any order. FIG. 8C describes defect analysis step 830 infurther detail. Specifically, in step 831, CDs for one or more featuresin the defect-free areas on the simulated wafer images 517 and for anyneighbor features in the defect areas on the simulated wafer images 517are measured.

To determine an ACD, the CD of one defect-free feature on simulatedwafer image (physical mask) 517A is first subtracted from the CD of thecorresponding defect-free feature on simulated wafer image (referencemask) 517B. This difference is then divided by the CD of the samedefect-free feature on simulated wafer image (reference) 517B. Toimprove the accuracy of the ACD calculation, multiple features can beanalyzed. Specifically, N ACDs can be added and then divided by N,wherein N is an integer greater than or equal to 1. For example, if twofeatures were analyzed, then the ACD could be calculated by thefollowing equation: [(CD(R1)−CD(P1))/CD(R1)+[(CD(R2)−CD(P2))/CD(R2)]/2),wherein R represents the simulated wafer image of the reference mask andP represents the simulated wafer image of the physical mask. Note thatACDs could be determined for different defect-free features or for thesame defect-free feature. For example, in one embodiment, arepresentative gate can be cut every 2 nm (parallel slices of FETchannel) across the gate width. Note that the CD estimation can beperformed using standard mask inspection equipment provided byKLA-Tencor, Applied Materials, LaserTech, or any other reticleinspection/metrology tool vendor.

In one embodiment, different exposures can be used to provide multipleACDs for each feature. Note that the exposures used can be a range ofvalues that deviate from the exposure level that will be used in theactual fabrication process, thereby providing a user with valuableinformation regarding the worst-case result. Further note that suchexposure conditions are typically included in lithography conditions 512(FIG. 5) that can be simulated. Therefore, referring back to FIG. 8C,the ACD for each exposure can be calculated in step 832.

In step 833, a relative CD deviation (RCD) is calculated for eachidentified neighbor feature (as identified in the defect areas of thesimulated images) in each exposure. For example, for each exposure, theCD of an identified neighbor feature (such as 904(R)) in defect area901(R) of simulated wafer image 901(R) is subtracted from the CD of thesame feature (in this case, 904(P)) in defect area 901(P) on simulatedwafer image 901(P). This difference is then divided by the CD of theidentified neighbor feature on simulated wafer image 901(R) (i.e.(CD(P)−CD(R))/CD(R). Finally, the maximum RCD (MCD) of the identifiedneighbor feature can be determined for each exposure in step 834.

As described in reference to FIG. 3, a feature in a critical region,such as a gate, requires a high degree of precision to ensure properperformance of the transistor in the final IC device. Thus, by analyzingmultiple masks and the features therein, a defect could be characterizedas insubstantial because it is small and in a non-critical region (e.g.the interconnect), whereas a defect could be characterized assubstantial even though it is small because it is in a critical regionof the IC (e.g. the gate).

Referring to FIG. 5, defect printability analysis generator 515 alsoreceives information from a critical region identification generator514. Critical region identification generator 514 could include anystandard pattern recognition tools (both hardware and software) toanalyze the physical masks, like physical mask 501A, used to fabricatethe IC. Irrespective of specific tools used, critical regionidentification generator 514 provides defect printability analysisgenerator 515 with information identifying areas of each mask that aredesignated critical regions. With this information, defect printabilityanalysis generator 515 can determine whether a defect is located withina critical region in step 840 (FIG. 8A).

A defect in a critical region will typically have a lower tolerance forrelative CD changes. In one embodiment, the tolerance for CD changes(TCD) could be provided by a look-up table. This look-up table couldinclude values determined by a mask quality control engineer based onexperience and various mask specifications. For example, a critical areacould have a TCD range between approximately 3% and 5%, whereas anon-critical area with very few features could have a TCD range betweenapproximately 10% and 15%. In one embodiment, critical region IDgenerator 514 could include this look-up table.

Equation 1 provides an illustrative calculation for determining a defectseverity score. Note that Equation 1 includes the ACD, MCD, and TCDvariables described in detail above, and further includes a variable ithat indicates a particular exposure and a variable N that indicates thetotal number of exposures analyzed.

$\begin{matrix}{{D\; S\; S} = {( {3/N} ) \times {\overset{N}{\sum\limits_{1}}{{( {{M\; C\; D_{i}} - {A\; C\; {D_{i}/3}}} )/T}\; C\; D}}}} & {{Equation}\mspace{20mu} 1}\end{matrix}$

FIG. 9A illustrates an exemplary portion 900(P) from a physical mask.Portion 900(P) includes a defect area 901(P) and a defect-free area902(P). In a similar manner, FIG. 9B illustrates a portion 900(R) from areference mask corresponding to portion 900(P). Portion 900(R) includesa defect area 901(R) and a defect-free area 902(R).

By using coordinates or defect-free patterns in pre-processing step 810,the corresponding locations of these portions/areas in the simulatedwafer images can be aligned. Specifically, for example, the simulatedwafer images of defect-free areas 902(P) and 902(R) can be aligned. In asimilar manner the simulated wafer images of defect areas 901(P) and901(R) can be aligned. Once areas 901 and 902 are aligned, the featuresin the simulated wafer images are also aligned. Thus, for example,features 904(P) and 905(P) of defect area 901(P) are aligned withfeatures 904(R) and 905(R) of defect area 901(R) in pre-processing step810.

In two-dimensional analysis step 820, a defect on the simulated waferimage of mask portion 901(P) is identified. In this example, an arrowpoints to a defect 903 in defect area 901(P). Features 904(P) and 905(P)are then identified as neighbor features that can be affected by defect903. Finally, any corresponding features on the simulated wafer image ofmask portion 901(R) can be identified. In this example, features 904(R)and 905(R) are identified.

At this point, defect analysis step 830 can proceed. FIGS. 10 (A(1)-A(3)and B(1)-B(3)) and 11 (A(1)-A(3) and B(1)-B(3)) illustrate theapplication of the defect severity score calculation to the simulatedwafer images of portions 900(P) and 900(R). To calculate the average CDdeviations for defect-free features, multiple features are typicallymeasured. For example, FIGS. 10A(1-3) represent the simulated waferimages from defect-free area 902(P) of physical mask 900(P) for threeexposures. Lines 1001(P)-1006(P) represent cuts made to two defect-freefeatures of the simulated wafer image in the three exposures.Specifically, lines 1001(P) and 1002(P) represent cuts made to twofeatures in a first exposure, lines 1003(P) and 1004(P) represent thesame cuts made to the same features in a second exposure, and lines1005(P) and 1006(P) represent the same cuts made to the same features ina third exposure.

In a similar manner, FIGS. 10B(1-3) represent simulated wafer imagesfrom defect-free area 902(R) of reference mask 900(R) for the same threeexposures. Lines 1001(R)-1006(R) represent cuts made to two defect-freefeatures of the simulated wafer image in the three exposures, whereinthese cuts correspond to the cuts 1001(P)-1006(P). Thus, lines 1001(R)and 1002(R) represent cuts made to two features in a first exposure,lines 1003(R) and 1004(R) represent the same cuts made to the samefeatures in a second exposure, and lines 1005(R) and 1006(R) representthe same cuts made to the same features in a third exposure.

Each cut line 1001(P)-1006(P) and 1001(R)-1006(R) provides an associatedCD. Therefore, for ease of reference, lines 1001(P)-1006(P) and1001(R)-1006(R) are hereinafter referenced as CDs 1001(M)-1006(M) and1001(R)-1006(R).

For the first exposure shown in FIGS. 10A(1) and 10B(1), the average CDdeviation can be calculated as follows:

ACD(1)=[(1001(R)−1001(P))/1001(R)+(1002(R)−1002(P))/1002(R)]/2

In one embodiment, the actual measurements of CDs 1001(R), 1001(P),1002(R), and 1002(P) are, respectively, 266 nm, 266 nm, 322 nm, and 294nm. Plugging these values into the equation for ACD(1), yieldsapproximately 0.043 nm.

For the second exposures shown in FIGS. 10A(2) and 10B(2), the averageCD deviation can be calculated in a similar manner:

ACD(2)=[(1003(R)−1003(P))/1003(R)+(1004(R)−1004(P))/1004(R)]/2

In one embodiment, the actual measurements of CDs 1003(R), 1003(P),1004(R), and 1004(P) are, respectively, 266 nm, 266 nm, 294 nm, and 294nm. Plugging these values into the equation for ACD(2), yields 0.0 nm.

Finally, for the third exposure shown in FIGS. 10A(3) and 10B(3), theaverage CD deviation can also be calculated in the same manner:

ACD(3)=[(1005(R)−1005(P))/1005(R)+(1006(R)−1006(P))/1006(R)]/2

In one embodiment, the actual measurements of CDs 1005(R), 1005(P),1006(R), and 1006(P) are, respectively, 252 nm, 238 nm, 294 nm, and 294nm. Plugging these values into the equation for ACD(3), yieldsapproximately 0.028 nm.

In defect analysis, a relative CD deviation (RCD) is also calculated forneighbor features in the defect area for each exposure level. FIGS.11A(1-3) illustrate the simulated wafer images for features 904(P) and905(P) in defect area 901(P) for three exposures. Lines 1101(P)-1106(P)represent cuts made to these two features of the simulated wafer imagein the three exposures. Specifically, lines 1101(P) and 1102(P)represent cuts made to features 904(P) and 905(P) in a first exposure,lines 1103(P) and 1104(P) represent cuts made to features 904(P) and905(P) in a second exposure, and lines 1105(P) and 1106(P) representcuts made to features 904(P) and 905(P) in a third exposure.

Similarly, FIGS. 11B(1-3) illustrate the simulated wafer images forfeatures 904(R) and 905(R) for the same three exposures. Lines1101(R)-1106(R) represent cuts made to these two features of thesimulated wafer image in the three exposures. Specifically, lines1101(R) and 1102(R) represent cuts made to features 904(R) and 905(R) ina first exposure, lines 1103(R) and 1104(R) represent cuts made tofeatures 904(R) and 905(R) in a second exposure, and lines 1105(R) and1106(R) represent cuts made to features 904(R) and 905(R) in a thirdexposure.

Each line 1101(P)-1106(P) and 1101(R)-1106(R) provides an associated CD.Therefore, for ease of reference, lines 1101(P)-1106(P) and1101(R)-1106(R) are hereinafter referenced as CDs 1101(P)-1106(P) and1101(R)-1106(R).

For the first exposures shown in FIGS. 11A(1) and 11B(1), the relativeCD deviation (RCD) can be calculated for feature 904 as follows:

RCD(1(904))=(1101(P)−1101(R))/1101(R)

In one embodiment, the actual measurements of CDs 1101(R) and 1101(P)are, respectively, 266 nm and 364 nm. Plugging these values into theequation for RCD(1(904)), yields approximately 0.368 nm.

In a similar manner, for the first exposures shown in FIGS. 11A(1) and11B(1), the relative, maximum CD (RCD) change can be calculated forfeature 905 as follows:

RCD(1(905))=(1102(P)−1102(R))/1102(R)

In one embodiment, the actual measurements of CDs 1102(R) and 1102(P)are, respectively, 252 nm and 322 nm. Plugging these values into theequation for RCD(1(905)), yields approximately 0.278 nm.

The RCDs can be calculated for features 904 and 905 for the second andthird exposures in a similar manner as shown below.

RCD(2(904))=(1103(P)−1103(R))/1103(R)

RCD(2(905))=(1104(P)−1104(R))/1104(R)

RCD(3(904))=(1105(P)−1105(R))/1105(R)

RCD(3(905))=(1106(P)−1106(R))/1106(R)

In one embodiment, the actual measurements of CDs 1103(R), 1103(P),1104(R), 1104(P), 1105(R), 1105(P), 1106(R), 1106(P) are, respectively,238 nm, 350 nm, 252 nm, 294 nm, 224 nm and 280 nm. Plugging these valuesinto the equations for RCD(2(904)), RCD(2(905)), RCD(3(904)), andRCD(3(905)), yields, respectively, approximately 0.471 nm, 0.167 nm,0.353 nm, and 0.250 nm.

To determine the maximum CD deviation (MCD) for each exposure, thelargest RCD value is chosen. Thus, the maximum CD deviation for thefirst exposure (MCD(1)) is 0.368 nm (0.368>0.278), MCD(2) is 0.471 nm(0.471>0.167), and MCD(3) is 0.353 nm (0.353>0.250).

The defect severity score (DSS) can be calculated by using Equation 1.In the example given, because three exposures were analyzed, N=3.

Substituting these values in Equation 1 yields:

${D\; S\; S} = {( {3/3} ) \times {\overset{3}{\sum\limits_{1}}{{( {{M\; C\; D_{i}} - ( {A\; C\; {D_{i}/3}} )} )/T}\; C\; D}}}$

Thus, based on the three exposures,

DSS=(3/3)[(MCD(1)−(ACD(1)/3))/TCD+(MCD(2)−(ACD(2)/3))/TCD+(MCD(3)−(ACD(3)/3))/TCD]

Substituting the values calculated above for MCD and ACD for the threeexposures, yields:

DSS=[(0.368−(0.043/3))/0.1+(0.473−(0/3))/0.1+(0.353−(0.028/3))/0.1

DSS=3.54+4.73+3.44

Therefore, defect 903 (see FIG. 9A) has a DSS of approximately 11.71.

Defect printability analysis generator 515 (FIG. 5) can output thedefect severity score (DSS) (in one embodiment, a scale from 1 to 10) inan impact report 516. This impact report 516 can be used to reduce humanerror in defect printability analysis. For example, perhaps a DSS scoreof 5 indicates that printed features would have significant performanceissues, but that repair of the physical mask is possible. On the otherhand, perhaps a DSS score of 7 and above indicates not only performanceissues, but that re-fabrication of the physical mask is recommended. Forexample, in one embodiment, a DSS of less than 3 means that the CDchanges due to the defect are within the specified CD tolerance, a DSSbetween 3 and 6 means that the CD changes due to the defect are largerthan the specified CD tolerance, but the CD changes do not result insevere defects on the wafer (such as opens or bridges), and a DSSgreater than 6 means that the CD changes due to the defect result insevere defects on the wafer. Thus, by providing a numerical resulthaving an associated meaning for each number, a technician can proceedefficiently and without error to the next action, e.g. repair of thephysical mask or re-fabrication of the physical mask.

Process Windows

Defect printability can also be assessed using various process windows.Process windows can be derived from certain plots known by those skilledin the art. In general terms, the process window of a feature is theamount of variation in the process that can be tolerated while stillmaintaining critical dimensions (CDs) of the feature within a certainrange of the target CD.

One known process variation is the focus setting of the projection tool,i.e. the stepper. The focus can significantly change the resist profile(CD, sidewall angle, and resist thickness) and thus is critical inproviding an acceptable lithographic process.

Because of the impact of focus and exposure, these variables aretypically varied at the same time in a focus-exposure matrix. Theprocess window can be derived from such a matrix. Focus and exposurevalues that fall within the process window produce resist features, e.g.CDs, within tolerance, whereas focus and exposure values that falloutside the process window produce resist features outside tolerance.Thus, as shown in detail below, the process window can provide anobjective means for determining the severity and printability of adefect.

For example, FIG. 12A illustrates a mask having a feature 1204 and adefect 1203. As shown above, defect 1203 will affect the width offeature 1204. Specifically, the width of feature 1204 at cut line 1201will be larger than the width at cut line 1202.

FIG. 12B shows a plot of feature size (also in nanometers) versusdefocus (in nanometers). In this figure, the bold horizontal lineindicates that the target CD is 200 nm, whereas the other horizontallines indicate a +/−10% error of this target CD. Curves 1211 and 1212are generated by exposing (or simulating exposure of) the mask includingdefect 1223 and analyzing the CDs of the printed feature at cut lines1201 and 1202 at various defocus levels (in this case, −500 nm to 500nm). Curves 1211 and 1212 represent the CD analysis at cut lines 1201and 1202, respectively.

Logically, each feature size on curve 1212 has a corresponding largerfeature size on curve 1211. For example, at −300 nm defocus, the featuresize at cut line 1202 (see curve 1212) is approximately 150 nm, whereasthe feature size at cut line 1201 (see curve 1211) is approximately 170nm. Note that an acceptable defocus window for both curves, i.e. betweenhorizontal lines CD +/−10%, is between approximately −208 nm and 208 nm.

FIG. 12C illustrates a plot of percentage exposure deviation versusdefocus (in nanometers). In this figure, curves 1221 represent the upperand lower bounds of exposure deviation for cut line 1201 for variousdefocus levels, whereas curves 1222 represent the upper and lower boundsof exposure deviation for cut line 1202 for various defocus levels. Thelargest possible rectangle that fits within the overlap of these twoareas defines a common process window 1223. In this embodiment, commonprocess window 1223 indicates that the defocus can vary betweenapproximately −150 nm and 150 nm, whereas the exposure deviation canvary between approximately −10% and 10% (all while maintaining the lineCD within tolerance).

FIG. 12D plots exposure latitude (%) versus depth of focus (DOF) (innanometers), wherein exposure latitude refers to the amount of exposuredose variation and DOF refers to the amount of focus variation. In thisfigure, curves 1231 represent the upper and lower bounds of exposurelatitude for cut line 1201 for various DOFs, whereas curves 1232represent the upper and lower bounds of exposure latitude for cut line1202 for various DOFs. Note that curves 1231 and 1232 share the samelower boundary. The largest possible rectangle that fits under thecommon lower boundary defines a common process window 1233. In thisembodiment, common process window 1233 indicates that the DOF can varybetween approximately 0 nm and 300 nm, whereas the exposure latitude canvary between approximately 0% and 19% (once again, while maintaining theline CD within tolerance).

Note that the information provided by process window 1233 can be derivedby process window 1223. Specifically, the DOF range is equal to thetotal range of defocus and the exposure latitude range is equal to thetotal range of exposure deviation.

FIG. 13A illustrates a mask having a feature 1304 and a defect 1303.Although feature 1304 is the same size as feature 1204, defect 1303 issignificantly larger than defect 1203. Thus, the width of printedfeature 1304 at cut line 1301 will be wider than the width of printedfeature 1304 at cut line 1302. Moreover, as described in detail below,defect 1303 will significantly decrease the process window compared todefect 1203.

FIG. 13B shows a plot of feature size (also in nanometers) versusdefocus (in nanometers). Once again, the bold horizontal line indicatesthat the target CD is 200 nm, whereas the other horizontal linesindicate a +/−10% error of this target CD. Curves 1311 and 1312 aregenerated by exposing (or simulating exposure of) the mask includingdefect 1323 and analyzing the CDs of the printed feature at cut lines1301 and 1302 at various defocus levels (in this case, −500 nm to 500nm). Curves 1311 and 1312 represent the CD analysis at cut lines 1301and 1302, respectively.

As noted previously, each feature size on curve 1312 has a correspondinglarger feature size on curve 1311. For example, at −300 nm defocus, thefeature size at cut line 1302 (see curve 1312) is approximately 150 nm,whereas the feature size at cut line 1301 (see curve 1311) isapproximately 185 nm. Note that an acceptable defocus window for bothcurves, i.e. between horizontal lines CD +/−10%, is betweenapproximately −208 nm and −100 nm as well as between approximately 100nm and 208 nm.

FIG. 13C illustrates a plot of percentage exposure deviation versusdefocus (in nanometers). In this figure, curves 1321 represent the upperand lower bounds of exposure deviation for the CD corresponding to cutline 1301 for various defocus levels, whereas curves 1322 represent theupper and lower bounds of exposure deviation for the CD corresponding tocut line 1302 for various defocus levels. The largest possible rectanglethat fits within the overlap of these two areas defines a common processwindow 1323. In this embodiment, common process window 1323 indicatesthat the defocus can vary between approximately −100 nm and 100 nm,whereas the exposure deviation can vary between approximately 2% and 15%(all while maintaining the line CD within tolerance).

FIG. 13D plots exposure latitude (%) versus DOF (in nanometers). In thisfigure, curves 1331 represent the upper and lower bounds of exposurelatitude for the CD corresponding to cut line 1301 for various DOFs,whereas curves 1332 represent the upper and lower bounds of exposurelatitude for the CD corresponding to cut line 1302 for various DOFs.Note that curves 1331 and 1332 share substantially the same upper andlower boundaries. The largest possible rectangle that fits under thecommon lower boundary defines a common process window 1333. In thisembodiment, common process window 1333 indicates that the DOF can varybetween approximately 0 nm and 200 nm, whereas the exposure latitude canvary between approximately 0% and 12% (once again, while maintaining theline CD within tolerance).

Note that process windows 1223/1233 are significantly larger thanprocess windows 1323/1333. As seen from this example, a larger defectsize reduces the process window. Therefore, various process windows canbe compared to determine defect printability. Specifically, the processwindow for a defect-free feature could be compared to one or moreprocess windows of features having a defect proximate to suchfeature(s). In a typical embodiment, the customer could set a range ofacceptable deviation from the process window for the defect-freefeature.

The process described above is equally applicable to defects that formpart of the feature. For example, FIG. 14A illustrates a mask having afeature 1404 and an integrally formed defect 1403. Defect 1403 willaffect the width of feature 1404. Specifically, the width of feature1404 at cut line 1401 will be larger than the width at cut line 1402.

FIG. 14B shows a plot of feature size (also in nanometers) versusdefocus (in nanometers). In this figure, the bold horizontal lineindicates that the target CD is 200 nm, whereas the other horizontallines indicate a +/−10% error of this target CD. Curves 1411 and 1412are generated by exposing (or simulating exposure of) the mask includingdefect 1403 and analyzing the CDs of the printed feature at cut lines1401 and 1402 at various defocus levels (in this case, −500 nm to 500nm). Curves 1411 and 1412 represent the CD analysis at lines 1401 and1402, respectively. In this embodiment, energy of 3.9 mJ/cm2 was assumedfor development.

Logically, each feature size on curve 1412 has a corresponding largerfeature size on curve 1411. For example, at −300 nm defocus, the featuresize at cut line 1402 (see curve 1412) is approximately 150 nm, whereasthe feature size at cut line 1401 (see curve 1411) is approximately 165nm. Note that an acceptable defocus window for both curves, i.e. betweenhorizontal lines CD +/−10%, is between approximately −208 nm and 208 nm.

FIG. 14C illustrates a plot of percentage exposure deviation versusdefocus (in nanometers). In this figure, curves 1421 represent the upperand lower bounds of exposure deviation for cut line 1401 for variousdefocus levels, whereas curves 1422 represent the upper and lower boundsof exposure deviation for cut line 1402 for various defocus levels. Thelargest possible rectangle that fits within the overlap of these twoareas defines a common process window 1423. In this embodiment, commonprocess window 1423 indicates that the defocus can vary betweenapproximately −150 nm and 150 nm, whereas the exposure deviation canvary between approximately −5% and 9% (all while maintaining the line CDwithin tolerance).

FIG. 14D plots exposure latitude (%) versus DOF (in nanometers). In thisfigure, curves 1431 represent the upper and lower bounds of exposurelatitude for cut line 1401 for various DOFs, whereas curves 1432represent the upper and lower bounds of exposure latitude for cut line1402 for various DOFs. Note that curves 1431 and 1432 share the samelower boundary. The largest possible rectangle that fits under thecommon lower boundary defines a common process window 1433. In thisembodiment, common process window 1433 indicates that the DOF can varybetween approximately 0 nm and 300 nm, whereas the exposure latitude canvary between approximately 0% and 14% (once again, while maintaining theline CD within tolerance).

As noted previously, the information provided by process window 1433 canbe derived by process window 1423. Specifically, the DOF range is equalto the total range of defocus and the exposure latitude range is equalto the total range of exposure deviation.

FIG. 15A illustrates a mask having a feature 1504 and a defect 1503.Although feature 1504 is the same size as feature 1404, defect 1503 issignificantly larger than defect 1403. Thus, the width of printedfeature 1504 at cut line 1501 will be wider than the width of printedfeature 1404 at cut line 1401. Moreover, as described in detail below,defect 1503 will significantly decrease the process window compared todefect 1403.

FIG. 15B shows a plot of feature size (also in nanometers) versusdefocus (in nanometers). Once again, the bold horizontal line indicatesthat the target CD is 200 nm, whereas the other horizontal linesindicate a +/−10% error of this target CD. Curves 1511 and 1512 aregenerated by exposing (or simulating exposure of) the mask includingdefect 1503 and analyzing the CDs of the printed feature at cut lines1501 and 1502 at various defocus levels (in this case, −500 nm to 500nm). Curves 1511 and 1512 represent the CD analysis at cut lines 1501and 1502, respectively.

As noted previously, each feature size on curve 1512 has a correspondinglarger feature size on curve 1511. For example, at −300 nm defocus, thefeature size at cut line 1502 (see curve 1512) is approximately 150 nm,whereas the feature size at cut line 1501 (see curve 1511) isapproximately 198 nm. Note that an acceptable defocus window for bothcurves, i.e. between horizontal lines CD +/−10%, is no longerattainable.

FIG. 15C illustrates a plot of percentage exposure deviation versusdefocus (in nanometers). In this figure, curves 1521 represent the upperand lower bounds of exposure deviation for the CD corresponding to cutline 1501 for various defocus levels, whereas curves 1522 represent theupper and lower bounds of exposure deviation for the CD corresponding tocut line 1502 for various defocus levels. In this case, the two areasdefined by curves 1521 and 1522 do not overlap. Therefore, no commonprocess window exists. Thus, defect 1503 will effectively preventfeature 1504 from printing within tolerance.

FIG. 15D plots exposure latitude (%) versus DOF (in nanometers). In thisfigure, curve 1531 represents the upper bounds of exposure latitude forthe CD corresponding to cut line 1501 for various DOFs, whereas curve1532 represents the upper bounds of exposure latitude for the CDcorresponding to cut line 1502 for various DOFs. Note that curves 1531and 1532 do not share any lower boundary. Therefore, no common processwindow exists, thereby confirming the information derived from FIG. 15D.

In FIGS. 12-15, the use of process windows to determine defectprintability has been applied to lines. However, this use of processwindows is also applicable to the printability of contacts and vias.FIGS. 16A-16D respectively illustrate a defect-free contact 1601 on amask, a plot 1602 of feature size versus defocus, a plot 1603 ofexposure deviation versus defocus (and a resulting process window), anda plot 1604 of exposure latitude versus DOF (and its resulting processwindow).

In contrast, FIGS. 17A-17D respectively illustrate a contact 1701 on amask, a plot 1702 of feature size versus defocus, a plot 1703 ofexposure deviation versus defocus (and a resulting process window), anda plot 1704 of exposure latitude versus DOF (and its resulting processwindow). Note that contact 1701 has noticeably significant CD variationsand thus, under prior art analysis, could be considered a moderatelydefective contact.

However, an analysis of the process windows of FIGS. 17C and 17Dcompared to the process windows of FIGS. 16C and 16D reveals thatcontact 1701, although exhibiting considerable CD variation, has oneprocess window relatively similar to those of contact 1601.Specifically, referring to FIGS. 17D and 16D, contacts 1701 and 1601both have a common depth of focus between 0 and 600 nm and asubstantially similar exposure latitude, i.e. between 0 and 58% forcontact 1701 and between 0 and 40% for contact 1601. However, althoughcontacts 1701 and 1601 have the identical defocus (i.e. between −300 nmand 300 nm), these contacts have significantly different percentageexposure deviations. Specifically, contact 1701 has an exposuredeviation between approximately 22% and 80%, whereas contact 1601 has anexposure deviation between approximately −3 and 37%. As a result, acommon process window, albeit small, can exist for both contacts 1701and 1601.

Thus, analyzing the process windows associated with a feature canprovide an objective means of determining the printability of thatfeature based on a defect. For example, a defect severity can be derivedfrom the amount of overlap between two process windows (i.e. the commonprocess window), wherein one process window is extracted from the defectcut line and another process window is extracted from the reference cutline. Specifically, in accordance with this embodiment, defectprintability analysis generator 515 could determine a common processwindow for the features provided in masks 501A and 501B and provide thisinformation in impact report 516.

Repairs of the Physical Mask

FIG. 18 illustrates one process that can be used to analyze repairs thatcould be done on a physical mask. As shown in FIG. 18, using impactreport 516 (or portions therein), a bitmap editor 1801 can indicatepossible corrections to be made to the physical mask (for example,physical mask 501A) to eliminate or significantly minimize the effectsof one or more defects. Bitmap editor 1801 can then output a simulatedmask 1802 including these corrections. Thus, simulated mask 1802 is apossible, repaired version of the physical mask. Note that bitmap editor1801 can be separate from or use the same tools as wafer image generator509.

Simulated mask 1802 can be inspected by inspection tool 502 and used bywafer image generator 509 to generate a new, simulated wafer image (notshown) and a new impact report that indicates the success of thepossible corrections provided in mask 1802. If the corrections areacceptable, then bitmap editor 1801 can provide the correctioninformation directly to mask repair tools 1803 for repair of thephysical mask. If the customer desires additional optimization oranalysis of different parameters, then the above-described processes canbe repeated until either the corrections are deemed to be within anacceptable range or bitmap editor 1801 indicates that the desired resultcannot be attained by repairing the physical mask.

In one embodiment, bitmap editor 1801 can also indicate the optimizedmask writing strategy. For example, a laser tool can be used for opaquedefects (e.g. removal of chromium defects), whereas a focused ion beamtool can be used for clear defects (e.g. deposition of chromium). Notethat laser and focused ion beam tools can also be used for depositionand removal, respectively. Generally, a focused ion beam tool providesmore precision than a laser tool. However, the focused ion beam tool istypically slower than the laser tool. Bitmap editor 1801 can receiveinputs (not shown) that indicate customer time or cost limitations,thereby allowing bitmap editor 1801 to optimize the repair process basedon these customer parameters.

In yet another embodiment of the invention, bitmap editor 1801 can beused to provide information to wafer repair tools (not shown).Specifically, bitmap editor 1801 can include a program that compares theefficacy of repairing a mask versus repairing a wafer. In oneembodiment, the program can convert non-optical (e.g. SEM, focused ionbeam) images into optical images for further analysis.

Batch Processing

Of importance, defect printability analysis can be done on individualdefects or on a plurality of defects. In one embodiment, inspection tool502 and wafer image generator 509 can automatically provide outputsregarding all defects found on physical mask 501A. Thus, impact report516 could include defect severity scores on all defects.

Alternatively, if desired, impact report 516 could include only defectseverity scores above a certain value (above DSS of “5”, for example).This tailored impact report could, in turn, be provided to bitmap editor1801 (and subsequently to mask repair tools 1803). Therefore, acomplete, automated defect detection and correction process could beprovided, thereby significantly reducing the time for a mask to beanalyzed and repaired (if appropriate).

OPC Considerations

Defect printability analysis can also eliminate the necessity ofevaluating OPC features separately from other features. For example,assume that a defect located proximate to a scattering bar does noteffect the printing of an associated isolated feature. However, thisdefect may optically interact with the scattering bar, thereby resultingin printing at least part of the scattering bar. As noted earlier, OPCfeatures, such as scattering bars, are sub-resolution features andshould not print.

In accordance with one embodiment, if an OPC feature prints (asdetermined by simulated wafer image) due to a defect, then the defectanalysis (step 830) can indicate this error as CD changes are determined(step 831). Thus, by eliminating any complicated design rules regardingOPC features, this embodiment ensures a quick, reliable, and accuratemethod to identify defects adversely affecting OPC features.

Mask Quality Issues

In addition to CD changes, other printability factors, such as line edgeroughness, also should factor into mask quality. However, line edgeroughness of a feature on a mask is currently not measured in ameaningful way.

FIG. 19A illustrates a simplified, simulated wafer image 1900 includingtwo lines 1901 and 1902. Of interest, a line with line edge roughnessmight not necessarily exhibit CD variations. For example, because line1902 has substantially symmetrical line edge roughness, line 1902 mightnot have significant CD variations. However, both lines 1901 and 1902should be characterized as exhibiting line edge roughness.

Referring to FIG. 19B, edges of a simulated line can be analyzedseparately, thereby allowing line edge roughness to be accuratelymeasured. Specifically, using line 1902 as an example, a centerline 1903of line 1902 can be determined based on reference mask 501B (FIG. 5).Then, a plurality of theoretical cuts are made to line 1902 (indicatedby lines 1904). Each line 1904 includes two “ribs” that extend from thecenterline to opposite edges of the line. For example, rib 1904R extendsfrom centerline 1903 to the right edge of line 1902, whereas rib 1904Lextends from centerline 1903 to the left edge of line 1902. Note thatribs 1904R and 1904L when added equal the CD of line 1902.

As one feature of the invention, the lengths of the ribs on each side ofcenterline 1903 can be measured independently. In this manner, line edgeroughness can be accurately determined for each edge of line 1902. Inone embodiment, defect printability analysis generator 515 can calculatethe DSS of the LER by using the equations explained in detail inreference to FIGS. 8A-8C, but modifying those equations to substituterib length for a CD. Because all lines unavoidably have some LER, defectprintability analysis generator 515 could include a look-up tableindicating a threshold value for the LER. If unacceptable line edgeroughness (LER) is detected, defect printability analysis generator 515could indicate the LER of line 1902 as a “defect” listed in impactreport 516. Thus, the LER could be repaired in a manner similar to thatdescribed above in reference to FIG. 18.

Advantageously, the method of using a centerline and ribs can be appliedto other features on a mask. For example, even the most perfect contacton a mask can print as a circle or near circle on the wafer because ofdiffraction. Using high power electron beam (e-beam) lithography forprinting the contact on the wafer minimizes this diffraction. However,e-beam lithography is significantly more expensive and slower than theindustry-standard laser raster scan. Unfortunately, using the rasterscan results in corner rounding of many, but not necessarily all, thecontacts on the layout. Such corner rounding can be efficientlydetected, as described in detail below.

FIG. 20A illustrates a contact 2000 having a centerline 2001, lines 2002including ribs 2002TR (top right), 2002BR (bottom right), 2002TL (topleft), and 2002BL (bottom left), and lines 2003 including ribs 2003R(right) and 2003L (left). In accordance with one feature of theinvention, a plurality of theoretical, horizontal cuts made to contact2000 can be spaced unequally, thereby providing more data points forparticular elements of a feature.

In this example, corner rounding of contacts is of particular interest.Therefore, the spacing of the cuts is modified to ensure a sufficientnumber of data points to analyze particularly in the corners of thecontact. Thus, in FIG. 20A, lines 2002 have a closer spacing than lines2003. Corner rounding for contact 2000 can be determined by comparingthe lengths of ribs 2003L to the lengths of ribs 2002TL for the top leftcorner and to the lengths of ribs 2002 BL for the bottom left corner. Ina similar manner, the lengths of ribs 2003R can be compared to thelengths of ribs 2002TR for the top right corner and to the lengths ofribs 2003BR for the bottom right corner. Note that there are severalknown methods of estimating corner rounding effects (e.g. missing area,normal distance histogramming, etc.)

In some cases, performance issues relating to contacts can includewhether a symmetrical contact shape is consistently made on the wafer.Advantageously, in addition to line edge roughness, the symmetry of thecontact can also be determined. For example, the lengths of ribs 2002TL,2003L, and 2002BL can be compared to the lengths of ribs 2002TR, 2003R,and 2002BR to determine the horizontal symmetry of contact 2000 fromcenterline 2001. The vertical symmetry of contact 2000 can be determinedby using vertical cuts as shown in FIG. 20B and following a similarprocess of rib comparison. The overall symmetry of contact 2000 (i.e.the “squareness”) can be determined by comparing selected combinedhorizontal ribs (for example, the added lengths of one of ribs 2002TLand one of ribs 2002TR, i.e. the CD) to selected combined vertical ribs.

In one embodiment, defect printability analysis generator 515 cancalculate the DSS of the symmetry by using the equations explained indetail in reference to FIGS. 8A-8C, but modifying those equations tosubstitute rib length for a CD. Because all contacts unavoidably havesome non-symmetry, defect printability analysis generator 515 couldinclude a look-up table indicating a threshold value for thenon-symmetry. If unacceptable symmetry is detected, defect printabilityanalysis generator 515 could indicate the contact/via as a “defect”listed in impact report 516. Thus, the symmetry could be repaired in amanner similar to that described above in reference to FIG. 18.

Note that some structures on the layout, such as hammerheads and serifs(outer and inner corner), are provided to facilitate the accuratetransferring of lines on the mask to the wafer. These structures,although not printing independently from the lines, can affect CDvariations of those lines on the wafer. Thus, any variations of thesestructures due to printing the mask can also adversely affect theprinting of the lines associated with those structures on the wafer. Byexamining the CD variations or corner rounding of the lines havingassociated structures, the quality of these structures can alsoeffectively be analyzed.

OTHER EMBODIMENTS

Defect printability analysis, defect severity score, and mask qualityassessment are described above in various embodiments. Variations andmodifications to those embodiments will be apparent to those skilled inthe art. For example, as described above, a physical mask and acorresponding, defect-free reference image are inspected. In oneembodiment described above, the defect-free reference image is asimulated image of the layout of the physical mask. In an alternativeembodiment, the defect-free reference image is a defect-free area of thephysical mask having the same pattern. In yet another embodiment, thedefect-free reference image is a simulated image of the mask as it isprocessed in manufacturing. In yet another embodiment, the defect-freereference image is a physical mask image as compensated for withmicroscope (lens) effects.

In a standard mask fabrication process, such as that shown in FIG. 1,the defect printability/mask quality analysis described above can beincluded in mask inspection step 116. Additionally, the mask qualityanalysis described above is equally applicable to wafer repairprocesses. For example, after the wafer fails inspection as determinedin step 124, rather than proceeding to mask repair steps 128 and 130,process steps for repairing the wafer could be added. In yet anotherembodiment, in addition to the defect severity score, impact report 516(FIG. 5) could include other performance output, such as cross-sectioncontour lines, light intensity data, critical dimensions at differentdefocuses, and phase transmission data including effect on criticaldimensions. Therefore, the present invention is limited only by theattached claims.

1. A method of generating a defect severity score report for a physicalmask, the method comprising: identifying a defect on a simulated waferimage of the physical mask; identifying a corresponding area on asimulated wafer image of a reference mask, the reference maskcorresponding to a defect-free physical mask; identifying a set ofneighbor features proximate to the defect on the simulated wafer imageof the physical mask; identifying a corresponding set of neighborfeatures on the simulated wafer image of the reference mask; measuringcritical dimensions (CDs) for predetermined defect-free features on thesimulated wafer image of the physical mask at different exposures;measuring CDs for corresponding predetermined defect-free features onthe simulated wafer image of the reference mask at the differentexposures; calculating an average CD deviation for the predetermineddefect-free features and the corresponding defect-free features for eachexposure; calculating the defect severity score using the average CDdeviation for each exposure; and generating the defect severity scorereport with the defect severity score.
 2. The method of claim 1, furtherincluding: measuring CDs for any identified neighbor feature on thesimulated wafer image of the physical mask at the different exposures;measuring CDs for any corresponding identified neighbor feature on thesimulated wafer image of the reference mask at the different exposures;and calculating a relative CD deviation for each identified neighborfeature and the corresponding identified neighbor feature for eachexposure, wherein calculating the defect severity score includes usingthe relative CD deviation for each exposure.
 3. The method of claim 2,further including: determining a maximum relative CD deviation for eachexposure, wherein calculating the defect severity score includes usingthe maximum relative CD deviation for each exposure.
 4. The method ofclaim 3, wherein the defect severity score being within a first rangeindicates that CD changes due to the defect are within a specified CDtolerance.
 5. The method of claim 4, wherein the defect severity scorebeing within a second range indicates that CD changes due to the defectare larger than a specified CD tolerance, but the defect is not a severedefect.
 6. The method of claim 5, wherein the defect severity scorebeing within a third range indicates that CD changes due to the defectare larger than a specified CD tolerance, and the defect is a severedefect.
 7. The method of claim 3, wherein the defect severity scorebeing within a first range indicates that the physical mask should berepaired.
 8. The method of claim 7, wherein the defect severity scorebeing within a second range indicates that the physical mask should bere-fabricated.
 9. The method of claim 8, wherein the defect severityscore being within a third range indicates that the physical mask isacceptable.
 10. A computer readable medium comprising computerinstructions that, when run on a computer, generate signals to controlthe process steps of: generating a simulated wafer image of a physicalmask, the physical mask including a defect; generating a simulated waferimage of a reference mask, the reference mask corresponding to adefect-free physical mask; computing an average critical dimension (CD)deviation for a defect-free area of the physical mask using thesimulated wafer images of the physical and reference masks; computing amaximum CD deviation for a defect area of the physical mask using thesimulated wafer images of the physical and reference masks; using theaverage CD deviation and the maximum CD deviation to provideprintability analysis regarding the defect of the defect area; andgenerating a printability report based on the printability analysis. 11.The computer readable medium of claim 10, wherein the printabilityanalysis includes a defect severity score for the defect, wherein thereport includes the defect severity score.
 12. The computer readablemedium of claim 11, wherein the defect severity score being within afirst range indicates that CD changes due to the defect are within aspecified CD tolerance.
 13. The computer readable medium of claim 12,wherein the defect severity score being within a second range indicatesthat CD changes due to the defect are larger than a specified CDtolerance, but the defect is not a severe defect.
 14. The computerreadable medium of claim 13, wherein the defect severity score beingwithin a third range indicates that CD changes due to the defect arelarger than a specified CD tolerance, and the defect is a severe defect.15. The computer readable medium of claim 11, wherein the defectseverity score being within a first range indicates that the physicalmask should be repaired.
 16. The computer readable medium of claim 15,wherein the defect severity score being within a second range indicatesthat the physical mask should be re-fabricated.
 17. The computerreadable medium of claim 16, wherein the defect severity score beingwithin a third range indicates that the physical mask is acceptable. 18.A method of generating a printability report for a physical mask, themethod comprising: generating a simulated wafer image of the physicalmask, the physical mask including a defect; generating a simulated waferimage of a reference mask, the reference mask corresponding to adefect-free physical mask; identifying a first feature proximate to thedefect on the simulated wafer image of the physical mask; identifying asecond feature on the simulated wafer image of the reference mask, thesecond feature corresponding to the first feature; computing criticaldimension (CD) deviations including the first and second features toprovide printability analysis; and generating the printability reportbased on the printability analysis.
 19. The computer readable medium ofclaim 18, wherein computing CD deviations includes computing a defectseverity score for the defect, and wherein the printability reportincludes the defect severity score.
 20. The computer readable medium ofclaim 19, wherein the defect severity score being within a first rangeindicates that CD changes due to the defect are within a specified CDtolerance.
 21. The computer readable medium of claim 20, wherein thedefect severity score being within a second range indicates that CDchanges due to the defect are larger than a specified CD tolerance, butthe defect is not a severe defect.
 22. The computer readable medium ofclaim 21, wherein the defect severity score being within a third rangeindicates that CD changes due to the defect are larger than a specifiedCD tolerance, and the defect is a severe defect.
 23. The computerreadable medium of claim 19, wherein the defect severity score beingwithin a first range indicates that the physical mask should berepaired.
 24. The computer readable medium of claim 23, wherein thedefect severity score being within a second range indicates that thephysical mask should be re-fabricated.
 25. The computer readable mediumof claim 24, wherein the defect severity score being within a thirdrange indicates that the physical mask is acceptable.
 26. A method ofgenerating a printability report for a physical mask, the methodcomprising: generating a simulated wafer image of the physical mask, thephysical mask including a defect; generating a simulated wafer image ofa reference mask, the reference mask corresponding to a defect-freephysical mask; computing an average critical dimension deviation for adefect-free area of the physical mask using the simulated wafer imagesof the physical and reference masks; computing a maximum criticaldimension deviation for a defect area of the physical mask using thesimulated wafer images of the physical and reference masks; using theaverage critical dimension deviation and the maximum critical dimensiondeviation to provide the printability analysis; determining a defectseverity score based on the step of using; and generating theprintability report including the defect severity score.
 27. The methodof claim 26, wherein the defect severity score being within a firstrange indicates that CD changes due to the defect are within a specifiedCD tolerance.
 28. The method of claim 27, wherein the defect severityscore being within a second range indicates that CD changes due to thedefect are larger than a specified CD tolerance, but the defect is not asevere defect.
 29. The method of claim 28, wherein the defect severityscore being within a third range indicates that CD changes due to thedefect are larger than a specified CD tolerance, and the defect is asevere defect.
 30. The method of claim 26, wherein the defect severityscore being within a first range indicates that the physical mask shouldbe repaired.
 31. The method of claim 30, wherein the defect severityscore being within a second range indicates that the physical maskshould be re-fabricated.
 32. The method of claim 31, wherein the defectseverity score being within a third range indicates that the physicalmask is acceptable.